Appendix 2. PRISMA Flow Chart Outlining Review.pdf
Bibliographic record
Abstract
Under the happy-productive worker hypothesis, organizations invest significant resources in employee well-being with the expectation of organizational benefits. However, more evidence is needed to understand the extent wellness interventions generate mutual gains for both well-being and work outcomes. This review combines systematic and realist approaches to examine 154 individual-level wellness intervention studies and the contextual factors that enable—or limit—their success. Drawing from management training literature, we apply Holton’s model of learning transfer, which emphasizes the role of individual and contextual factors in shaping transfer motivation, transfer design, and the transfer climate. We find that wellness interventions consistently enhance employee well-being but do not reliably lead to improvements in workplace outcomes, such as performance. Our analysis identifies the program mechanisms that support training transfer and contribute to mutual gains: transfer design is enabled when interventions have program characteristics that reflect the work context; transfer motivation was bolstered when organizations gave thoughtful consideration of participants’ needs and engagement strategies; and transfer climate was enabled by factors like supervisor support and organizational culture that reinforced cultural fit. Theoretical and practical implications are discussed, emphasizing context-sensitive interventions that optimize wellness programs for learning transfer to enable mutual gains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.112 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.501 | 0.052 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".